I often get asked to present impervious cover projections and changes over time a community during NEMO programs. We often refer to the 1990, 2000 land use data from the U of MN GIS lab land.umn.edu. However, is anyone using LIDAR to calculate and show impervious cover in a community? Watershed? Subwatershed? Or other geographic scale?

Hope things are well!There are some companies that offer this service but I don't know of anyone who is actively pursuing it on a city or other scale.

From what I have read, researched and seen, LiDAR alone doesn’t get you very far for automated identification of impervious surfaces. What I have seen requires a combination of imagery and lidar in conjunction with some high level image processing and classification software.

I have seen a couple of presentations on using LiDAR for mapping imperviousness and I have been tangentially involved with some other efforts at mapping imperviousness using high-resolution, multi-spectral imagery. The key piece of data from LiDAR for mapping imperviousness is the intensity of the reflected pulse. It works because most LiDAR systems use a laser pulse in the near-infrared portion of the electromagnetic spectrum. The typical approach involves separating green, vegetated areas from non-green areas. Because chlorophyll has a strong reflectance peak in the near-infrared region of the spectrum, data from near-infrared LiDAR can be helpful for mapping imperviousness.

LiDAR does have some potential advantages for mapping imperviousness over multi-spectral aerial imagery or satellite imagery. For instance, imagery relies on passive solar radiation and thus dark shadows from trees and buildings can often fool the algorithm classifying these areas as impervious surface. Because LiDAR has an active radiation source, it may help eliminate this confusion. However, there are a couple of caveats.

First, the strongest correlations for imperviousness are generally derived from well-known vegetation indices (the correlations are actually an inverse relationship). Such indices include the Tasseled Cap Greenness Index and the Normalized Difference Vegetation Index. While information on near infrared reflectance derived from LiDAR could be used to help calculate these indices, you also need information from other spectral regions (e.g. red wavelength is also required for NDVI). As such, the efforts I have seen using LiDAR to map imperviousness have necessarily incorporated high-resolution, multi-spectral imagery.

Second, much of the power to separate out pervious areas from impervious area relies on the strength of the reflectance signal from chlorophyll. However, LiDAR imagery is typically acquired during a period of vegetative dormancy (leaf-off). Summer, leaf-on data would probably be preferable from the perspective of separating out the spectral differences; however, you then lose some ability to map impervious surfaces beneath the vegetative canopy.

Third, the other thing you have to sort out is water, which like many impervious surfaces has a low reflectivity. Much of this can be handled by using an ancillary GIS source to mask out water features.

Probably the optimal approach would include imagery from multiple seasons along with LiDAR intensity. The difference between first return and last return might also be useful. Also maybe using an object-oriented approach would be good too. I know a few people who I could put you in touch with that could do this.